We present ongoing work as part of an international multidisciplinary project, called MiDRASH, on the computational analysis of medieval manuscripts. We focus here on clustering manuscripts written in Ashkenazi square script using a dataset of 206 pages from 59 manuscripts. Collaborating with expert paleographers, we identified ten critical features and trained a multi-label CNN, achieving high accuracy in feature prediction. This should make it possible to computationally predict the subclusters already known to paleographers and those yet to be discovered. We identified visible clusters using PCA and ^2 feature selection. In future work, we aim to enhance feature extraction using deep learning algorithms and provide computational tools to ease paleographers' work. We plan to develop new methodologies for analyzing Hebrew scripts and refining our understanding of medieval Hebrew manuscripts.