General two-dimensional (2D) lattices have been used previously to represent the macromolecular structure of coal because capturing the full structural diversity is challenging. Advances now allow for the creation of large-scale multimolecule atomistic representations, including a greater range of structural diversity; however, visualizing the content of these large-scale structures in a meaningful manner is highly restrictive. A computational tool was previously created that enables a systematic simplification of complex three-dimensional (3D) structural models of coal into corresponding 2D lattice representations via pattern recognition and graph theory. The approach was used here on several large-scale models of coal and shows the significant reduction of scale and enhanced visualization capabilities of the properties of the models. While the view is simplistic, this "coarse-graining" approach retains all of the structural and chemical information of the reduced units, facilitating chemical information access for visualization purposes. The visualization capabilities of 2D lattice models of coal were demonstrated through the use of color schemes to portray the chemical properties of the original complex large-scale atomistic representations of various coals. Coal model-specific lattices displaying properties, such as the molecular weight of aromatic clusters, cross-link classification, molecule position, and predicted solubility, were enabled via this approach.