Induced subgraph isomorphism search finds the occurrences of embedded subgraphs within a single large data graph that are strictly isomorphic to a given query graph. Labeled graphs contain object types and are a primary input source to this core search problem, which applies to systems like graph databases for answering queries. In recent years, researchers have employed GPU parallel solutions to this problem to help accelerate runtimes by utilizing the filtering-and-joining framework, which first filters vertices that cannot be part of the solution then joins partial solutions with candidate edges until full isomorphisms are determined. However, the performance of current GPU-based solutions is hindered by limited filtering effectiveness and presence of extraneous computations. This paper presents G-Morph, a fast GPU-based induced subgraph isomorphism search system for labeled graphs. Our filtering-and-joining system parallelizes both phases by upfront eliminating irrelevant vertices via a novel space-efficient vertex signature hashing strategy and efficiently joining partial solutions through use of a novel sliding window algorithm (slide-join) that provides overflow protection for larger input. Together these techniques greatly reduce extraneous computations by reducing Cartesian products and improving edge verification while supporting large scan operations (split-scan). G-Morph outperforms the state-of-the-art GPU-based GSI and CPU-based VF3 systems on labeled real-world graphs achieving speedups of up to 15.78x and 43.56x respectively.