Searching agents have achieved significant progress in NLP, yet their multimodal counterparts are constrained by a lack of suitable evaluation benchmarks requiring reasoning over continuous video, audio, and images. We propose long-form TV dramas as a natural solution and introduce DramaSearch, encompassing 3,600 riddles across 9 dramas totaling 239 hours. Our dataset is generated via a graph-constrained pipeline that processes enriched captions into structured events and multi-granularity evidence views. By employing character-to-view and entity-to-view indexing, we ensure controllable clue disclosure and strictly unique answers. The riddles are categorized into multi-clue, multi-hop, and compositional types, requiring agents to aggregate isolated clues and navigate complex dependency chains. Experiments demonstrate that a Qwen3-8B agent, trained on a held-out set using SFT and RL, achieves an accuracy of 48.3%. This result establishes a strong baseline that performs favorably against zero-shot GPT-5 (37.0%), while indicating significant room for future research. All data and code will be publicly released.