Post-earthquake investigations show that unreinforced masonry (URM) buildings may exhibit diverse failure mechanisms depending on the construction morphology and the connection detailing between their structural components. Advanced computational models are necessary to consider the influence of these aspects. However, realistically reproducing the post-collapse state of an existing URM building is challenging when limited data is available on the aforementioned features. To address this challenge, a framework for exploring the seismic behavior of URM buildings is presented. The current investigation presents two case study buildings located in Türkiye's Hatay province: the Mithatpaşa Primary School in Iskenderun and the Liwan Boutique Hotel in Antakya, both of which suffered partial collapses during the recent Kahramanmaraş Earthquakes in 2023. Discrete block models of the two case study buildings are generated based on geometrical information obtained from various pre- and post-collapse vision-based data sources. An automatic block generation algorithm is proposed to replicate periodic and nonperiodic masonry wall patterns. Next, the generated discrete block media are analyzed using discontinuum-based structural analysis to predict the seismic response of the structures. Comparisons between the preliminary pushover analysis results and collapse observations inform further analyses, and lead to an exploration of how construction morphology and connection detailing may have contributed to the partial collapse of the buildings. It is demonstrated that this iterative approach, supported by forensic site evidence and reverse engineering analysis, provides new insight into the influence of key factors that contribute to collapse. This information can help safeguard similar structures and inform the development of effective retrofitting solutions.