We have adapted the M3GNet graph representation to describe organic molecular crystals both with and without angle featurization for a graph neural network framework. Similar to Taniguchi et al. we examined the influence of the number of nearest neighbors and cutoff radius on performance and found that the "MediumGraph" representation, 12 nearest neighbors and cutoff radius of 8 Å, was best. We trained GNN models using both the OCELOT and OMDB with and without angle featurization. We further fine-tuned the models trained on the OMDB with the OCELOT database. The models were then evaluated on the polymorphs of ROY as well as an augmented version of the PAH101.