At mesoscale, trade wind clouds organize with various spatial arrangements, shaping their effect on Earth's energy budget. Representing their fine-scale dynamics even at 1 km scale climate simulations remains challenging. However, geostationary satellites (GS) offer high-resolution cloud observation for gaining insights into trade wind cumuli from long-term records. To capture the observed organizational variability, this work proposes an integrated framework using a continuous followed by discrete self-supervised deep learning approach, which exploits cloud optical depth from GS measurements. We aim to simplify the entire mesoscale cloud spectrum by reducing the image complexity in the feature space and meaningfully partitioning it into seven classes whose connection to environmental conditions is illustrated with reanalysis data. Our framework facilitates comparing human-labeled mesoscale classes with machine-identified ones, addressing uncertainties in both methods.  It advances previous methods by exploring transitions between regimes, a challenge for physical simulations, and illustrates a case study of sugar-to-flower transitions.