Graph neural networks (GNNs) are a machine learning framework where graphs are fed as inputs into convolution-type neural networks. Graphs are made up of nodes and edges that connect those nodes. For chemistry and materials science applications, atoms are mapped to nodes and bonds are mapped to edges. Hence, this is a fairly straightforward and intuitive way to represent structures for machine learning. Several GNNs have shown great success in property prediction, such as atomistic line graph neural network (ALIGNN), crystal graph convolutional neural network (CGCNN), materials graph neural network (MEGNet), , and SchNet. Previous work has examined the performance of some of these GNNs in predicting the bandgap of OSCs. Taniguchi et al. used the MatDeepLearn framework developed by Fung et al. to measure the performance of CGCNN, MEGNet, and SchNet on a dataset curated by Olsthorn et al. from the Organic Materials DataBase (OMDB). The OMDB consists of organic molecular crystals whose properties have been calculated with DFT. They found that the MEGNet model exhibited the best performance, with a mean absolute error of 0.240 eV. However, they noted that their model struggled with polymorphic systems, such as the highly polymorphic 5-Methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile (ROY). They attributed this to a few reasons, including the training data did not consist of any sets of polymorphs and (2) the graph representation did not include angular information, such as the angle between bonds. In terms of (1), our previous work examined the performance of the GNN models CGCNN, MEGNet, and SchNet, as well as ALIGNN and graph attention graph neural network (GATGNN), on the Organic Crystals in Electronic and Light-Oriented Technologies (OCELOT) database. OCELOT is a database composed of organic molecular crystals extracted from the Cambridge Structural Database (CSD) and community contributions. These structures are then relaxed with dispersion-inclusive density functional theory. OCELOT has been used previously in machine learning work for property prediction and materials discovery for OSCs. OCELOT also has several hundred sets of polymorphs and is of a similar size to the dataset curated by Olsthorn et al. The results showed some improvement on Taniguchi et al. for the polymorphic ROY, with a lowest MAE of 0.237 eV using SchNet compared to Taniguchi's 0.40 eV using MEGNet. However, there was still little correlation between the polymorph of ROY and the predicted bandgap, suggesting that the graph representation may not be capturing all the relevant features for accurate predictions. Hence, we now turn our attention to problem (2) of Taniguchi et al. in this work.